近红外光谱结合Lasso算法测定制浆材抽出物含量

吴　珽<sup>1</sup>; 房桂干<sup>1; *</sup>; 梁　龙<sup>1</sup>; 崔宏辉<sup>1</sup>; 熊智新<sup>2</sup>

吴　珽,房桂干,梁　龙,崔宏辉,熊智新.近红外光谱结合Lasso算法测定制浆材抽出物含量[J].中国造纸学报,2015,30(4):22-26

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近红外光谱结合Lasso算法测定制浆材抽出物含量

The Prediction of Pulpwood Extractives Content by Near Infrared Spectroscopy Combining with Lasso Algorithm

DOI：10.11981/j.issn.1000-6842.2015.04.22

中文关键词: Lasso算法近红外光谱制浆材抽出物含量

Key Words:Lasso algorithm near-infrared spectroscopy pulpwood extractive content

基金项目:国家林业局948项目“农林剩余物制机械浆节能和减量技术引进”(2014-4-31)。

作者	单位
吴　珽¹	1.中国林业科学研究院林产化学工业研究所,国家林业局林产化学工程重点开放性实验室,生物质化学利用国家工程实验室,江苏南京,210042
房桂干^1,*	1.中国林业科学研究院林产化学工业研究所,国家林业局林产化学工程重点开放性实验室,生物质化学利用国家工程实验室,江苏南京,210042
梁　龙¹	1.中国林业科学研究院林产化学工业研究所,国家林业局林产化学工程重点开放性实验室,生物质化学利用国家工程实验室,江苏南京,210042
崔宏辉¹	1.中国林业科学研究院林产化学工业研究所,国家林业局林产化学工程重点开放性实验室,生物质化学利用国家工程实验室,江苏南京,210042
熊智新²	2.南京林业大学轻工科学与工程学院,江苏南京,210037

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中文摘要:

为实现制浆材材性的快速测定,首先用常规方法测定了144个制浆材样品的冷水、热水、苯-醇和1% NaOH抽出物含量,并采集了样品的近红外光谱,然后对原始光谱进行预处理,并运用Lasso算法及交互验证建立最优校正模型。对模型进行独立验证,决定系数R2val分别为0.9186、0.9085、0.9241、0.9760,预测均方根误差分别为0.24%、0.30%、0.28%、0.38%,相对分析误差分别为3.50、3.31、3.63、6.45,绝对偏差分别为-0.42%～0.37%、-0.43%～0.41%、-0.47%～0.40%、-0.55%～0.57%。这些模型预测性能能够满足制浆造纸工业的要求,同时,也证实了Lasso算法用于制浆材抽出物测定的可行性。

Abstract:

The contents of cold water, hot water, benzene ethanol and 1.0% NaOH extractive of 144 pulpwood samples were analyzed using the traditional methods, meanwhile their near-infrared (NIR) spectra were also collected. After the pretreatment of original spectra, the optimal prediction models were established by using Lasso algorithm and cross-validation. The independent verification of the optimal prediction models showed the coefficients of determination (R2) were 0.9186, 0.9085, 0.9241 and 0.9760. The root mean square error of prediction (RMSEP) were 0.24%, 0.30%, 0.28% and 0.38%. The relative percent deviation (RPD) were 3.50, 3.31, 3.63 and 6.45. The absolute deviation (AD) were -0.42%～0.37%,-0.43%～0.41%,-0.47%～0.40%,-0.55%～0.57% respectively for cold water, hot water, benzene ethanol and 1.0% NaOH extractives. The prediction performance of the four models could meet the need of pulping and paper making industry and meanwhile Lasso algorithm was feasible for the prediction and analysis of pulpwood extractive content.

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